SoroushVahidi/scidocs huggingface dataset:
Programming by Feedback
(2014)

This paper advocates a new ML-based programming framework, called Programming by Feedback (PF), which involves a sequence of interactions between the active computer and the user. The latter only provides preference judgments on pairs of solutions supplied by the active computer. The active computer involves two components: the learning component estimates the user’s utility function and accounts for the user’s (possibly limited) competence; the optimization component explores the search space and returns the most appropriate candidate solution. A proof of principle of the approach is proposed, showing that PF requires a handful of interactions in order to solve some discrete and continuous benchmark problems.
Forwarding in a content-based network
(2003)

This paper presents an algorithm for content-based forwarding, an essential function in content-based networking. Unlike in traditional address-based unicast or multicast networks, where messages are given explicit destination addresses, the movement of messages through a content-based network is driven by predicates applied to the content of the messages. Forwarding in such a network amounts to evaluating the predicates stored in a router's forwarding table in order to decide to which neighbor routers the message should be sent. We are interested in finding a forwarding algorithm that can make this decision as quickly as possible in situations where there are numerous, complex predicates and high volumes of messages. We present such an algorithm and give the results of studies evaluating its performance.
Probabilistic in-network caching for information-centric networks
(2012)

In-network caching necessitates the transformation of centralised operations of traditional, overlay caching techniques to a decentralised and uncoordinated environment. Given that caching capacity in routers is relatively small in comparison to the amount of forwarded content, a key aspect is balanced distribution of content among the available caches. In this paper, we are concerned with decentralised, real-time distribution of content in router caches. Our goal is to reduce caching redundancy and in turn, make more efficient utilisation of available cache resources along a delivery path.
 Our in-network caching scheme, called ProbCache, approximates the caching capability of a path and caches contents probabilistically in order to: i) leave caching space for other flows sharing (part of) the same path, and ii) fairly multiplex contents of different flows among caches of a shared path.
 We compare our algorithm against universal caching and against schemes proposed in the past for Web-Caching architectures, such as Leave Copy Down (LCD). Our results show reduction of up to 20% in server hits, and up to 10% in the number of hops required to hit cached contents, but, most importantly, reduction of cache-evictions by an order of magnitude in comparison to universal caching.
A survey on content-centric technologies for the current Internet: CDN and P2P solutions
(2012)

0140-3664/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.comcom.2011.10.005 q This work was partially funded by the European AWARE RECOGNITION (257756), FIRE-SCAMPI (25841 ⇑ Tel.: +39 0503153269. E-mail address: a.passarella@iit.cnr.it One of the most striking properties of the Internet is its flexibility to accommodate features it was not conceived for. Among the most significant examples, in this survey we consider the transition of the Internet from a reliable fault-tolerant network for host-to-host communication to a content-centric network, i.e. a network mostly devoted to support efficient generation, sharing and access to content. We survey this research area according to a top-down approach. We present a conceptual framework that encompasses the key building blocks required to support content-centric networking in the Internet. Then we describe in detail the two most important types of content-centric Internet technologies, i.e., Content-Delivery Networks (CDNs) and P2P systems. For each of them, we show how they cover the key building blocks. We then identify the functional components of CDN and P2P content management solutions, and discuss the main solutions proposed in the literature for each of them. We consider different types of content (both real time and non real time), and different networking environments (fixed, mobile, . . .). Finally, we also discuss the main recent research trends focused on how to design the Future Internet as a native content-centric network. 2011 Elsevier B.V. All rights reserved.
A data-oriented (and beyond) network architecture
(2007)

The Internet has evolved greatly from its original incarnation. For instance, the vast majority of current Internet usage is data retrieval and service access, whereas the architecture was designed around host-to-host applications such as telnet and ftp. Moreover, the original Internet was a purely transparent carrier of packets, but now the various network stakeholders use middleboxes to improve security and accelerate applications. To adapt to these changes, we propose the Data-Oriented Network Architecture (DONA), which involves a clean-slate redesign of Internet naming and name resolution.
Computational intelligence and tower defence games
(2011)

The aim of this paper is to introduce the use of Tower Defence (TD) games in Computational Intelligence (CI) research. We show how TD games can provide an important test-bed for the often under-represented casual games research area. Additionally, the use of CI in the TD games has the potential to create a more interesting, interactive and ongoing game experience for casual gamers. We present a definition of the current state and development of TD games, and include a classification of TD game components. We then describe some potential ways CI can be used to augment the TD experience. Finally, a prototype TD game based on experience-driven procedural content generation is presented.
The Validity of Presence as a Reliable Human Performance Metric in Immersive Environments
(2000)

Advances in interactive media technologies have reached the stage where users need no longer to act as passive observers. As the technology evolves further we will be able to engage in computer based synthetic performances that mimic events in the real or natural world. Performances in this context refers to plays, films, computer based simulations, engineering mock-ups, etc. At a more fundamental level, we are dealing with complex interactions of the human sensory and perceptual systems with a stimulus environment (covering visual, auditory and other components). Authors or rather directors of these new media environments will exploit computer generated perceptual illusions to convince the person (participant) who is interacting with the system that they are present in the environment. Taken to the limit the participant would not be able to distinguish between being in a real or natural environment compared with being in a purely synthetic environment. Unfortunately, the performance of current technology falls considerably short of even getting even close to providing a faithful perceptual illusion of the real world. Eventually, VR authoring tools will become more sophisticated and it will be possible to produce realistic virtual environments that match more closely with the real world. Some VR practitioners have attempted to collect data that they purport to represent the degree of presence a user experiences. Unfortunately, we do not yet have an agreed definition of presence and seem undecided whether it is a meaningful metric in its own right. This paper will examine the dangers of attempting to measure the 'presence' of a VR system as a one-dimensional parameter. The question of whether presence is a valid measure of a VR system will also be addressed. An important differentiating characteristic of VR systems compared with other human-computer interfaces are their ability to create a sense of 'being-in' the computer generated environment. Other forms of media such as film and TV are also known to induce a sense of 'being-in' the environment. Some VR practitioners have tended to use the term presence to describe this effect This means that people who are engaged in the virtual environment feel as though they are actually part of the virtual environment. In order to be perceptually present in a virtual environment it is first important to understand what it means to be present in the real-world. A good example of real world experience is that of a roller coaster ride. The sensory …
Using the opinion leaders in social networks to improve the cold start challenge in recommender systems
(2017)

The increasing volume of information about goods and services has been growing confusion for online buyers in cyberspace and this problem still continues. One of the most important ways to deal with the information overload is using a system called recommender system. The task of a recommender system is to offer the most appropriate and the nearest product to the user's demands and needs. In this system, one of the main problems is the cold start challenge. This problem occurs when a new user logs on and because there is no sufficient information available in the system from the user, the system won't be able to provide appropriate recommendation and the system error will rise. In this paper, we propose to use a new measurement called opinion leaders to alleviate this problem. Opinion leader is a person that his opinion has an impact on the target user. As a result, in the case of a new user logging in and the user — item's matrix sparseness, we can use the opinion of opinion leaders to offer the appropriate recommendation for new users and thereby increase the accuracy of the recommender system. The results of several conducted tests showed that opinion leaders combined with recommender systems will effectively reduce the recommendation errors.
Anomaly detection using DBSCAN clustering technique for traffic video surveillance
(2015)

Detecting anomalies such as rule violations, accidents, unusual driving and other suspicious action increase the need for automatic analysis in Traffic Video Surveillance (TVS). Most of the works in Traffic rule violation systems are based on probabilistic methods of classification for detecting the events as normal and abnormal. This paper proposes an un-supervised clustering technique namely Novel Anomaly Detection-Density Based Spatial Clustering of Applications with Noise (NAD-DBSCAN) which clusters the trajectories of moving objects of varying sizes and shapes. A trajectory is said to be abnormal if the event that never fit with the trained model. Epsilon (Eps) and Minimum Points (MinPts) are essential parameters for dynamically calculating the sum of clusters for a data point. The proposed system is validated using benchmark traffic dataset and found to perform accurately in detecting anomalies.
What you look at is what you get: eye movement-based interaction techniques
(1990)

In seeking hitherto-unused methods by which users and computers can communicate, we investigate the usefulness of eye movements as a fast and convenient auxiliary user-to-computer communication mode. The barrier to exploiting this medium has not been eye-tracking technology but the study of interaction techniques that incorporate eye movements into the user-computer dialogue in a natural and unobtrusive way. This paper discusses some of the human factors and technical considerations that arise in trying to use eye movements as an input medium, describes our approach and the first eye movement-based interaction techniques that we have devised and implemented in our laboratory, and reports our experiences and observations on them.
Depression Scale Recognition from Audio, Visual and Text Analysis
(2017)

Depression is a major mental health disorder that is rapidly affecting lives worldwide. Depression not only impacts emotional but also physical and psychological state of the person. Its symptoms include lack of interest in daily activities, feeling low, anxiety, frustration, loss of weight and even feeling of selfhatred. This report describes work done by us for Audio Visual Emotion Challenge (AVEC) 2017 during our second year BTech summer internship. With the increase in demand to detect depression automatically with the help of machine learning algorithms, we present our multimodal feature extraction and decision level fusion approach for the same. Features are extracted by processing on the provided Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) database. Gaussian Mixture Model (GMM) clustering and Fisher vector approach were applied on the visual data; statistical descriptors on gaze, pose; low level audio features and head pose and text features were also extracted. Classification is done on fused as well as independent features using Support Vector Machine (SVM) and neural networks. The results obtained were able to cross the provided baseline on validation data set by 17% on audio features and 24.5% on video features. Keywords—AVEC 2017, SVM, Depression, Neural network, RMSE, MAE, fusion, speech processing.
Attacking DDoS at the Source
(2002)

Distributed denial-of-service (DDoS) attacks present an Internet-wide threat. We propose D-WARD, a DDoS defense system deployed at source-end networks that autonomously detects and stops attacks originating from these networks. Attacks are detected by the constant monitoring of two-way traffic flows between the network and the rest of the Internet and periodic comparison with normal flow models. Mismatching flows are rate-limited in proportion to their aggressiveness. D-WARD offers good service to legitimate traffic even during an attack, while effectively reducing DDoS traffic to a negligible level. A prototype of the system has been built in a Linux router. We show its effectiveness in various attack scenarios, discuss motivations for deployment, and describe associated costs.
PAGE: A Partition Aware Engine for Parallel Graph Computation
(2015)

Graph partition quality affects the overall performance of parallel graph computation systems. The quality of a graph partition is measured by the balance factor and edge cut ratio. A balanced graph partition with small edge cut ratio is generally preferred since it reduces the expensive network communication cost. However, according to an empirical study on Giraph, the performance over well partitioned graph might be even two times worse than simple random partitions. This is because these systems only optimize for the simple partition strategies and cannot efficiently handle the increasing workload of local message processing when a high quality graph partition is used. In this paper, we propose a novel partition aware graph computation engine named PAGE, which equips a new message processor and a dynamic concurrency control model. The new message processor concurrently processes local and remote messages in a unified way. The dynamic model adaptively adjusts the concurrency of the processor based on the online statistics. The experimental evaluation demonstrates the superiority of PAGE over the graph partitions with various qualities.
Making flexible magnetic aerogels and stiff magnetic nanopaper using cellulose nanofibrils as templates.
(2010)

Nanostructured biological materials inspire the creation of materials with tunable mechanical properties. Strong cellulose nanofibrils derived from bacteria or wood can form ductile or tough networks that are suitable as functional materials. Here, we show that freeze-dried bacterial cellulose nanofibril aerogels can be used as templates for making lightweight porous magnetic aerogels, which can be compacted into a stiff magnetic nanopaper. The 20-70-nm-thick cellulose nanofibrils act as templates for the non-agglomerated growth of ferromagnetic cobalt ferrite nanoparticles (diameter, 40-120 nm). Unlike solvent-swollen gels and ferrogels, our magnetic aerogel is dry, lightweight, porous (98%), flexible, and can be actuated by a small household magnet. Moreover, it can absorb water and release it upon compression. Owing to their flexibility, high porosity and surface area, these aerogels are expected to be useful in microfluidics devices and as electronic actuators.
When the face reveals what words do not: facial expressions of emotion, smiling, and the willingness to disclose childhood sexual abuse.
(2002)

For survivors of childhood sexual abuse (CSA), verbal disclosure is often complex and painful. The authors examined the voluntary disclosure-nondisclosure of CSA in relation to nonverbal expressions of emotion in the face. Consistent with hypotheses derived from recent theorizing about the moral nature of emotion, CSA survivors who did not voluntarily disclose CSA showed greater facial expressions of shame, whereas CSA survivors who voluntarily disclosed CSA expressed greater disgust. Expressions of disgust also signaled sexual abuse accompanied by violence. Consistent with recent theorizing about smiling behavior, CSA nondisclosers made more polite smiles, whereas nonabused participants expressed greater genuine positive emotion. Discussion addressed the implications of these findings for the study of disclosure of traumatic events, facial expression, and the links between morality and emotion.
OpinionFinder: A System for Subjectivity Analysis
(2005)

Vancouver, October 2005. OpinionFinder: A system for subjectivity analysis Theresa Wilson‡, Paul Hoffmann‡, Swapna Somasundaran†, Jason Kessler†, Janyce Wiebe†‡, Yejin Choi§, Claire Cardie§, Ellen Riloff∗, Siddharth Patwardhan∗ ‡Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15260 †Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260 §Department of Computer Science, Cornell University, Ithaca, NY 14853 ∗School of Computing, University of Utah, Salt Lake City, UT 84112
Model-Based Quantitative Network Security Metrics: A Survey
(2017)

Network security metrics (NSMs) based on models allow to quantitatively evaluate the overall resilience of networked systems against attacks. For that reason, such metrics are of great importance to the security-related decision-making process of organizations. Considering that over the past two decades several model-based quantitative NSMs have been proposed, this paper presents a deep survey of the state-of-the-art of these proposals. First, to distinguish the security metrics described in this survey from other types of security metrics, an overview of security metrics, in general, and their classifications is presented. Then, a detailed review of the main existing model-based quantitative NSMs is provided, along with their advantages and disadvantages. Finally, this survey is concluded with an in-depth discussion on relevant characteristics of the surveyed proposals and open research issues of the topic.
Study of wheel slip and traction forces in differential drive robots and slip avoidance control strategy
(2014)

The effect of wheel slip in differential drive robots is investigated in this paper. We consider differential drive robots with two driven wheels and ball-type caster wheels that are used to provide balance and support to the mobile robot. The limiting values of traction forces for slip and no slip conditions are dependent on wheel-ground kinetic and static friction coefficients. The traction forces are used to determine the fraction of input torque that provides robot motion and this is used to calculate the actual position of the robot under slip conditions. The traction forces under no slip conditions are used to determine the limiting value of the wheel torque above which the wheel slips. This limiting torque value is used to set a saturation limit for the input torque to avoid slip. Simulations are conducted to evaluate the behavior of the robot during slip and no slip conditions. Experiments are conducted under similar slip and no slip conditions using a custom built differential drive mobile robot with one caster wheel to validate the simulations. Experiments are also conducted with the torque limiting strategy. Results from model simulations and experiments are presented and discussed.
Design and fabrication of W-band SIW horn antenna using PCB process
(2013)

A W-band SIW horn antenna is designed and fabricated using PCB process. Measured S11 reaches -15 dB at 84 GHz with bandwidth of 1 GHz and the maximum simulated gain is 9 dBi. The antenna is loaded with dielectric to increase gain and the effect of different loading lengths on gain is studied. The antenna is fed using a WR10 standard waveguide through a coupling slot in the SIW. The effect of changing the slot width on the return loss is studied. Good agreement is achieved between measured and simulated results. The proposed antenna is suitable for medical imaging and air traffic radar.
Aligning 3D models to RGB-D images of cluttered scenes
(2015)

The goal of this work is to represent objects in an RGB-D scene with corresponding 3D models from a library. We approach this problem by first detecting and segmenting object instances in the scene and then using a convolutional neural network (CNN) to predict the pose of the object. This CNN is trained using pixel surface normals in images containing renderings of synthetic objects. When tested on real data, our method outperforms alternative algorithms trained on real data. We then use this coarse pose estimate along with the inferred pixel support to align a small number of prototypical models to the data, and place into the scene the model that fits best. We observe a 48% relative improvement in performance at the task of 3D detection over the current state-of-the-art [34], while being an order of magnitude faster.
Adaptive Scale Selection for Multiscale Segmentation of Satellite Images
(2017)

With dramatically increasing of the spatial resolution of satellite imaging sensors, object-based image analysis (OBIA) has been gaining prominence in remote sensing applications. Multiscale image segmentation is a prerequisite step that splits an image into hierarchical homogeneous segmented objects for OBIA. However, scale selection remains a challenge in multiscale segmentation. In this study, we presented an adaptive approach for defining and estimating the optimal scale in the multiscale segmentation process. Central to our method is the combined use of image features from segmented objects and prior knowledge from historical thematic maps in a top-down segmentation procedure. Specifically, the whole image was first split into segmented objects, with the largest scale in a presupposition segmentation scale sequence. Second, based on segmented object features and prior knowledge in the local region of thematic maps, we calculated complexity values for each segmented object. Third, if the complexity values of an object were large enough, this object would be further split into multiple segmented objects with a smaller scale in the scale sequence. Then, in the similar manner, complex segmented objects were split into the simplest objects iteratively. Finally, the final segmentation result was obtained and evaluated. We have applied this method on a GF-1 multispectral satellite image and a ZY-3 multispectral satellite image to produce multiscale segmentation maps and further classification maps, compared with the state-of-the-art and the traditional mean shift algorithm. The experimental results illustrate that the proposed method is practically helpful and efficient to produce the appropriate segmented image objects with optimal scales.